PRISM: Distribution-Gated Flow Matching for Controllable Unpaired Image Translation

2026-08-06 · 16:26 UTC ·Elad Yoshai, Natan T. Shaked ·1 min read

Brief

PRISM (arXiv 2026-08-06) is a GAN-free flow-matching framework for unpaired image translation that replaces global diffusion guidance with a learned per-feature gate whose spatial prior is each source feature's standardized distance to the target distribution. The gate controls task-matched latent corruption and ODE transport timing (AdaIN for structure-preserving), yielding best Inception FID and KID on four of five benchmarks (AFHQ cat->dog, CelebA‑HQ, day->night, virtual staining), competitive on histopathology with nuclei-count ratio closest to ideal; full text not provided.

Why it matters

PRISM (GAN-free flow-matching) replaces global diffusion guidance with a learned per-feature gate whose spatial prior is each source feature's standardized distance to the target feature distribution; the gate controls initialization (mixing the real source latent with a task-matched corruption) and ODE transport timing, and can be overridden locally at inference via text or a detector without retraining.

Key details

Task-matched corruptions: content-anchored (AdaIN) for structure-preserving translation and partially anchored corruption for structure-changing translation; the gate lets PRISM preserve target-consistent features while freeing features far from the target.

Evaluated on five benchmarks (AFHQ cat->dog, CelebA-HQ appearance translation, day->night relighting, virtual staining, breast frozen->permanent histopathology), PRISM attained the best Inception FID and KID on four benchmarks and a competitive result on the fifth; on histopathology it produced the nuclei-count ratio closest to the ideal (ArXiv 2026-08-06).

Abstract

Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision. Many diffusion-based unpaired translators control preservation through a single global noise or guidance value applied across the image, which cannot separate content to keep from appearance to change. We present PRISM, a GAN-free flow-matching framework that replaces this global control with a learned per-feature gate. The gate's spatial prior is derived from each source feature's standardized distance to the target feature distribution, so features far from the target are freed while target-consistent features are preserved. The same gate controls both the initialization, which mixes the real source latent with a task-matched corruption, and the transport timing during Ordinary Differential Equation (ODE) integration. The corruption is matched to the task, content-anchored (AdaIN) for structure-preserving translation and partially anchored for structure-changing translation, and the gate can be overridden locally at inference from text or a detector without retraining, preserving important structures of the original image while still generating realistic results. We evaluate PRISM on five natural and biomedical benchmarks (AFHQ cat->dog, CelebA-HQ appearance translation, day->night relighting, virtual staining, and breast frozen->permanent histopathology). Among the evaluated methods under a shared same-split protocol, PRISM attains the best Inception FID and KID on four benchmarks and a competitive result on the fifth, and on histopathology yields the nuclei-count ratio closest to the ideal, supporting a favorable balance between target realism and structural preservation.